Welcome to the QChartist blog QChartist Community – Your way to financial freedom

Finishing a big project can feel surprisingly strange — a mix of relief, pride, and sometimes a bit of emptiness. That’s completely normal.

Don’t rush past the finish line. Take a moment to:

• reflect on what you achieved
• recognize the effort you put in
• celebrate in a way that feels good to you

Closing the chapter properly helps your mind move on.

Expect the “post-project dip”

After working toward a clear goal, it’s common to feel a drop in motivation or energy.
You had structure and purpose — now there’s a gap.

👉 This isn’t a problem, just a transition phase.

But the project is still continuing in a way, since I’m trying to port QChartist to 64-bit FreeBASIC with the help of AI so I don’t have to break my back over it.

Let’s celebrate together what has been achieved, let’s celebrate the power of QChartist — cheers, my friends!🍸

That’s it, we have done it! QChartist TA build 327 can now load most of the indicators available in Pandas-TA python.

change log:

• New (python) tens of indicators: python_indis_main (main canvas) and python_indi (separate canvas)

In the properties of the indicators you can choose between these python indicators:

— pandas-ta-classic (215) —
aberration, above, above_value, accbands, ad, adosc, adx, alma, amat, ao, aobv,
apo, argmax, argmin, aroon, atr, av, bbands, below, below_value, bias, bop, brar
, cagr, calmar_ratio, candle_color, category_files, cci, cdl, cdl_doji, cdl_insi
de, cdl_pattern, cdl_z, cfo, cg, chop, cksp, cmf, cmo, combination, coppock, cro
ss, cross_value, cti, dataclass, decay, decreasing, dema, df_dates, df_error_ana
lysis, df_month_to_date, df_quarter_to_date, df_year_to_date, dm, donchian, down
side_deviation, dpo, drawdown, ebsw, efi, ema, entropy, eom, er, erf, eri, fibon
acci, field, final_time, find_spec, fisher, fwma, geometric_mean, get_distributi
on, get_drift, get_offset, get_time, ha, high_low_range, hilo, hl2, hlc3, hma, h
wc, hwma, ichimoku, increasing, inertia, is_datetime64_any_dtype, is_datetime_or
dered, is_percent, jensens_alpha, jma, kama, kc, kdj, kst, kurtosis, kvo, linear
_regression, linreg, log_geometric_mean, log_max_drawdown, log_return, long_run,
 ma, macd, mad, massi, max_drawdown, mcgd, median, mfi, midpoint, midprice, mom,
 mtd, natr, non_zero_range, npAll, npAppend, npCorrcoef, npDot, npSum, nvi, obv,
 ohlc4, ones, optimal_leverage, pascals_triangle, pdist, percent_return, pgo, pp
o, psar, psl, pure_profit_score, pvi, pvo, pvol, pvr, pvt, pwma, qqe, qstick, qt
d, quantile, real_body, recent_maximum_index, recent_minimum_index, rma, roc, rs
i, rsx, rvgi, rvi, seterr, sharpe_ratio, short_run, signals, signed_series, simp
lefilter, sinwma, skew, slope, sma, smi, sortino_ratio, squeeze, squeeze_pro, ss
f, stc, stdev, stoch, stochrsi, supertrend, swma, symmetric_triangle, t3, tal_ma
, td_seq, tema, thermo, to_utc, tos_stdevall, total_time, trima, triu, trix, tru
e_range, tsi, tsignals, ttm_trend, ui, unsigned_differences, uo, variance, verif
y_series, vhf, vidya, volatility, vortex, vp, vwap, vwma, wcp, weights, willr, w
ma, xsignals, yf, ytd, zero, zlma, zscore

Enjoy!

• build 327

For the build 326 of QChartist TA:

• i installed theses python modules:
  python -m pip install numpy==1.24.3
  python -m pip install pandas==2.0.3
  python -m pip install pandas-ta-classic
• i added example4.py
  i tried example4.py : error…
  python -m pip uninstall numpy pandas pandas-ta-classic
  python -m pip install numpy==1.19.5
  python -m pip install pandas==1.1.5
  python -m pip install pandas-ta-classic –no-deps
  python -m pip install python-dateutil pytz
  python -m pip list
  python -m pip install mplfinance –no-deps
  python -m pip install matplotlib==3.3.4
  python -m pip install taleb –no-deps
  In your code, you just use import taleb as talib and it works the same way.
  python -m pip install scipy==1.9.1 –no-deps
• now i tried : python example4.py “YHOO1440.csv” “2014-01-01 00:00” “2
  015-06-01 16:00″ yes
  and yes it works, i managed to use pandas_ta on python 32-bit ! Chart saved as custom_chart.bmp and custom_chart.png

What is example4.py (which needs pandas):
This script is a command-line financial analysis tool designed to process stock/forex data from a CSV file, apply various technical indicators from two different libraries, and generate a visual candlestick chart saved as a high-compatibility image (.bmp).

Globally, the script operates in four distinct phases:

1. Data Initialization and Filtering

The script starts by taking four command-line arguments: a CSV filename, a start date, an end date, and a toggle for volume.

It loads the CSV into a Pandas DataFrame.

It standardizes the columns to Open, High, Low, Close, and Volume.

It filters the data to only include the date range you specified in the terminal.

2. Indicator Discovery

The script performs a “deep scan” of your installed libraries to see what tools are available:

pandas-ta-classic: It uses the inspect module to find every available function inside the library.

taleb (TA-Lib): It similarly scans for all uppercase built-in functions (standard for TA-Lib indicators like RSI, MACD, etc.).

It then prints a massive list of all combined indicators found so you know what you can choose from.

3. Interactive Configuration

This is the “human-in-the-loop” part of the script:

Selection: It prompts you to type which indicators you want (e.g., sma, rsi, bbands).

Parameters: For every indicator you pick, it displays the help documentation for that specific function and asks you for parameters (like length=20).

Calculation: It runs the math and appends the new data (the indicator values) as new columns in your main DataFrame.

4. Smart Visualization (Mplfinance)

The final phase is the most complex part of the code logic:

Panel Management: It automatically decides where to draw the indicators.

    Overlays: Indicators like Moving Averages or Bollinger Bands are drawn directly on top of the price candles (Panel 0).

    Oscillators: Indicators like RSI or MACD are given their own separate "panels" below the price chart so they don't clutter the main view.

Image Export: Instead of just popping up a window (which can be difficult in a Wine environment), it generates a plot, saves it as a .png, and then converts it to a .bmp. This is likely done for compatibility with older Windows image viewers or specific legacy software.

Pandas-TA is a popular and comprehensive Technical Analysis Library in Python 3 that leverages numba and numpy for accuracy and performance, and pandas for simplicity and bulk processing. The library contains more than 150 indicators and utilities as well as 60 Candlestick Patterns when TA Lib is installed.

Now thats a very good news that example4.py script works because we’ll be able to add and use all 130 Indicators and Utility functions from pandas ta to QChartist and much more !
More info here: https://github.com/Data-Analisis/Technical-Analysis-Indicators—Pandas

• build 326

QChartist TA becomes more and more professional thanks to its interaction with python scripts dedicated to traders.

For the build 325:

• I upgraded python to version 25.0.1 and added some useful python scripts for traders
  c:\QChartist\Python38-32>python -m pip install –upgrade pip
• i installed some modules :
  modules
  pycoingecko
  binance
  typing_extensions
  cryptography
  pip install –force-reinstall cryptography==3.4.8
  aiohttp
  binance.client
  python -m pip uninstall python-binance binance -y
  python -m pip install python-binance
  pybit
  screeninfo
• i tried :
  c:\QChartist\Python38-32>python example10.py YHOO1440.csv “2015-09-01 00:00” “20
  16-02-01 00:00″
  Saved chart to: full_chart.ps
  To convert to PNG, use:
  gs -dSAFER -dBATCH -dNOPAUSE -sDEVICE=pngalpha -r144 -sOutputFile=full_chart.p
  ng full_chart.ps
  to obtain a 1h chart: use python pcgeck.py with command line arguments –interval 5min –days 33 then use period converter of 12 in qchartist
  to obtain a 4h chart : use python pcgeck.py with command line arguments –interval 15min –days 90 then use period converter of 16 in qchartist
  to obtain a daily chart : use the obtained 4h chart and use period converter of 6 in qchartist

pcgeck.py :
This script is a Python tool designed to fetch historical market data for a specific cryptocurrency from the CoinGecko API and convert it into a structured OHLCV (Open, High, Low, Close, Volume) format saved as a CSV file.

example10.py :
This script is a data visualization tool that generates a stock or cryptocurrency chart from a CSV file. It not only plots the primary price data but also calculates and overlays common technical analysis indicators.

binancevol.py :
This script is a data acquisition tool that connects to the Binance Futures API to download historical market data for a specific trading pair.
This Script (binancevol.py) acts as a specialized data collector specifically for Binance Futures, providing a more granular view of order flow (Buy vs. Sell pressure) which isn’t present in your general CoinGecko data.

binancefetch.py :
The script binancefetch.py is a specialized data acquisition tool designed to fetch historical OHLCV (Open, High, Low, Close, Volume) market data from the Binance REST API and save it to a CSV file.
Volume Split: A core feature of this script is the calculation of Buy Volume and Sell Volume. It takes the total volume and the “Taker buy base asset volume” provided by the API to separate trading activity into distinct buying and selling pressure metrics.

okx.py
This script (okx.py) is a data-collection and aggregation tool designed to analyze recent trading activity for a cryptocurrency pair on the OKX exchange.
Global Functionality
Globally, the script fetches raw individual trade data, organizes it into 1-minute intervals, and calculates buying versus selling pressure to identify market trends.

All these python script will be useful to implement and integrate new professional grade functionalities to QChartist TA software for the future releases, for example tick data and trades data information, order book and order flow analysis, volume footprint charts and much more !

• build 325

The Cumulative Volume Delta (CVD) is an absolutely fundamental tool for many professional traders, especially those specializing in Day Trading or Scalping in high-liquidity markets (Futures, Crypto, Forex).

Unlike lagging indicators based on price (such as RSI or MACD), CVD gets to the root of market movement: Order Flow.

Why do professionals use it?

CVD measures the net difference between aggressive buying volume (market orders) and aggressive selling volume. While price shows where the market is, CVD shows who is in control.

• Identifying Absorption: This is its most powerful use. If the price is dropping while the CVD is rising, it means large “passive” buyers are absorbing all the aggressive selling. This is often a sign of an imminent reversal.
• Confirming Trend Strength: In a healthy trend, price and CVD move up together. If the price climbs but the CVD flattens or drops, the trend is losing steam—buyers are no longer “hitting the tape” aggressively.
• Spotting Divergences: Pros track divergences between price peaks and CVD peaks to anticipate false breakouts (bull traps/bear traps).

Who specifically uses it?

1. Footprint Traders: They use CVD alongside Footprint charts to see the cumulative pressure over an entire session.
2. Futures Traders (Indices/Gold/Oil): On these centralized exchanges, volume data is precise, making the CVD extremely reliable.
3. Crypto Traders: Given the volatility and the heavy impact of market orders and liquidations in crypto, CVD has become an industry standard.

Limitations to keep in mind

While powerful, CVD is not a magic formula. It does not account for limit orders (passive liquidity) that have not yet been executed. A CVD exploding to the upside can still crash against a “wall” of hidden sell orders that are only visible on an Order Book or a Heatmap.

Pro Tip: CVD is most effective on lower timeframes (Intraday). On longer horizons (Weekly/Monthly), its utility diminishes as the delta tends to mean-revert or wash out over time.

Good news: QChartist now integrates order flow indicator CVD in build 324 !
Change Log:
2026-02-12:

• New indicator for separate canvas: CVD (Cumulative Volume Delta)
  To make the indicator work, please first put the value of “counted bars:” (cntbarsedit)
  from 1000 to 3000 ; then you have to load your symbol on
  3 timeframes : Daily TF,1 Minute TF and 1 Hour TF (we visualize it on 1H)
  Dont forget to put counted bars back to 1000 after using this indicator
  I made this indicator greatly with the help of AI chat bot and it made me save
  a lot of time.
  On small caps cryptos, CVD should be >100k/<-100k before expecting a significant reversal
• Recommended trading system improved in the trading_system.zip file
• build 323

2026-03-11:

• Fixed the CVD indicator, now it displays correct values
  To make the indicator work, please first put the value of “counted bars:” (cntbarsedit)
  from 1000 to 3000 ; then you have to load your symbol on
  3 timeframes : Daily TF,1 Minute TF and 1 Hour TF (we visualize it on 1H)
  Dont forget to put counted bars back to 1000 after using this indicator
  On small caps cryptos, CVD should be >100k/<-100k before expecting a significant reversal
• Recommended trading system improved in the trading_system.zip file
• Decreased tfintervalh1 value to 17280000 in QChartist.ini the way latest bars are
  correctly downloaded on 1H charts
  (please note that you’ll have to save and restore your data source api keys in a safe place
  for this update)
• build 324

The approximation of CVD indicator in QChartist assumes that the Conviction of a candle represents the Delta of that period. It typically uses the lowest timeframe available (M1) to build the data for higher timeframes (like H1 or D1).

Why Approximation is “Good Enough”

While it isn’t “True Delta” (which requires a Centralized Exchange like the CME), approximation is highly effective because of Arbitrage.
Because high-frequency trading bots link the Spot FX or crypto market (which you trade) to the Futures market (where real Delta happens), the price movement on your QChartist chart almost perfectly mirrors the aggressive buying/selling on the exchange. Therefore, a green M1 candle on QChartist is a very reliable signal that aggressive buying occurred on the Futures exchange.

Since you know the M1 candle is a proxy for aggressive orders:

• CVD Rising + Price Rising: Healthy trend (Aggressors are pushing and succeeding).
• CVD Falling + Price Rising: Exhaustion (Aggressors are stopping, price is moving on low liquidity).
• CVD Flat + Price Moving: Low conviction (The “Big Money” isn’t participating in the move).